Navigating the Tradeoff Dilemma: Optimizing Conflicting Objectives for Next-generation Networks

The next-generation networks employing Coordinated Multi-Point (CoMP) transmissions face fundamental optimization challenges in addressing conflicting multi-objective dependencies when selecting the communicating base stations. The existing single-objective CoMP optimization approaches fail to address these inherent tradeoffs, as maximizing one performance objective often leads to degradation in others, resulting in a suboptimal network performance in highly dynamic and heterogeneous environments. In this paper, we propose the Tradeoff Reinforced Intelligence with Adaptive Decision (TRIAD) framework, where three conflicting objectives-quality of experience, connection density, and load balancing-are simultaneously considered while optimizing the multi-objective CoMP problem. The experimental analyses reveal that single-objective approaches excel in only one metric while degrading others. In contrast, our proposed TRIAD framework creates positive feedback loops that enhance overall network performance. Furthermore, TRIAD achieves substantial improvements over static clustering methods, validating the adaptive advantage of the proposed framework in a dynamic network setting.